Bring the power of LLMs to your applications
Composable is the only API-first platform for building GenAI/Large Language Model (LLM) applications. We enable enterprise teams to automate and augment their business processes and applications with LLM-powered tasks.
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Agility & Flexibility
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Structured Outputs
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Contextually Relevant Responses
Composable Brings Stability at Scale
The AI marketplace is complex and rapidly changing, which is leading enterprise organizations trust Composable to future-proof their LLM-enabled applications and services.
Composable removes the complexity of managing disparate APIs, security models, and prompt formats from different providers. Enterprise teams seamlessly connect to any of the major AI providers and access all LLM foundation models using our open-source connectors. We take care of the syntax and transformation needed for each LLM so that you can focus on what matters most: bringing value to your organization with the power of LLMs.
Composable Gives Structure to the Unstructured
Extracting structure from unstructured content is essential when answering questions with data that was not previously considered to be independent.
With Composable, you can easily define your task and output schema, allowing any tool or application to make use of the newly structured data.
Composable Reduces LLM Hallucinations
LLMs are known to hallucinate (aka make mistakes). The trustworthiness of the information requires experts to determine fact from fiction.
Composable provides an intelligent content store to pre-process content for retrieval-augmented generation (RAG) and a workflow engine for orchestrating durable generative AI processes.
End-to-end governance of LLM agents and LLM-powered tasks. Know which task is deployed, which application uses it, what it does, and what data has been sent & received.
Fine-grained security, keys restricted to tasks, short-lived restricted public keys, audit history with advanced search in runs, automated key rotation, and more.
Execute tasks on any inference provider and model through easy to use API endpoints. Use and reuse results, thanks to persistence, indexing, and search.
Combine several LLMs, and choose the appropriate distribution strategy: weighted load balancing, multi-head execution, mediator, or intelligent routing.
An intelligent content store to pre-process content for RAG and a workflow engine for orchestrating durable generative AI processes.
Integrate LLM-powered tasks. Expose interaction definitions as robust API endpoints, ensure top-notch schema validation, and minimize call latency.
Iteratively design tasks as a team, bring the business and developers together with version control and an audit trail that keeps track of all history.
Follow model performance, visualize result quality, follow speed, latency, and quality. Monitor availability and performance.
Prioritizing, Selecting, and Implementing LLM Use Cases
This comprehensive how-to guide explores the strategies, tactics, and best practices for selecting and deploying LLM use cases. It explores eight real-world use cases and explains the potential impact of LLM-powered tasks on business operations, efficiency, and innovation.
